#SeSe_Net: semi-supervised segmentation by deep learning
#by Zeng Zeng, Yang Xulei, Yu Qiyun, Yao Meng, Zhang Le,
#Pattern Recognition Letter, Aug. 2019
To repeat the results in the paper:
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Download one of the three datasets used in the paper, e.g., carvana dataset
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Create the following folder structures
../input
../input/car_data/
../input/car_data/train_images
../input/car_data/train_masks
../input/car_data/train_list.csv
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Run sse_train_step1.py (labelled samples)
3-1) train a unet model to generate various masks
3-2) train a resnet model by using the generated masks
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Run sse_train_step2.py (un-labelled samples)
4-1) resnet predict un-labelled samples and generate loss values to refine unet model